| Summary: | It is
very important to map the burned forest areas economically, quickly and with
the high accuracy of issues such as damage assessment studies, fire risk
analysis, and management of forest regeneration processes. Mapping burned areas
with a fast and easy-to-use method and high accuracy will be a very useful tool
for local forest management units. In this study, we developed the new
approach, for mapping burned areas. In this regard we use the segmentation
process to the image, then apply the random forest algorithm for obtaining the
map of the burned areas. For this purpose, we use the Landsat 8 image of the
Adrasan and Kumluca fires which occurred in 24 – 27 June 2016. The study
consisted of four steps. After the multi-resolution image segmentation was performed
on obtained image objects from Landsat 8 spectral bands, the image object
metrics such as spectral index and layer values were calculated for all image
objects. In the third step, a random forest classifier model was developed.
Then, the developed model applied to the test site for classification of the
burned area. The obtained results evaluated with confusion matrix based on the
randomly sampled points. According to the results, we obtained 0.089 commission
error (CE) with 0.014 omission error (OE). An overall accuracy was obtained as
0.99. The results show that this approach is very useful to be used to
determine burned forest areas.
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